TY - JOUR
T1 - Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence
JF - Proceedings of the National Academy of Sciences
JO - Proc Natl Acad Sci USA
SP - 11090
LP - 11095
M3 - 10.1073/pnas.1717720115
VL - 115
IS - 43
AU - Denison, Rachel N.
AU - Adler, William T.
AU - Carrasco, Marisa
AU - Ma, Wei Ji
Y1 - 2018/10/23
UR - http://www.pnas.org/content/115/43/11090.abstract
N2 - We must routinely make decisions based on uncertain sensory information. Sometimes that uncertainty is related to our own cognitive state, such as when we are not paying attention. Do our decisions about what we perceive take into account our attentional state? Or are we blind to such internal sources of uncertainty, leading to poor decisions and overconfidence? We found that human observers take attention-dependent uncertainty into account when categorizing visual stimuli and reporting their confidence in a task in which uncertainty is relevant for performance. Moreover, they do so in an approximately Bayesian fashion. Human perceptual decision-making can therefore, at least in some cases, adjust in a statistically appropriate way to external and internal sources of uncertainty.Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer’s attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer’s decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.
ER -